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Figure 7. Samples of MSFDE dataset-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

We performed groups of experiments to study the impact of ethnic group (race) in the accuracy of emotion recognition with three kinds of ethnic groups (Asian, Caucasian as African). So we have three experiments, each experiment has a neural network as a classifier, and each neural network has three layers where there are 16 neurons in the hidden layer except Asian network has 17 neurons (the best result with 17 neurons for Asians).

Figure 3. 46 points are selected on face elements to describe the emotions.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

The number of points and the position of points are not standardized, but it is depending on the features that will be extracted, and used for the classifier. Many researches use various number of points and positions based on their view about the feature to be considered [13] [18] [19]. Figure 3 shows the points we used.

Figure 1. A proposed approach-of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

Our proposed approach uses the face expression to detect the emotions through five steps that shows in Figure 1.

Figure. 2. Examples of Angry from different races. (A,B) African. (C,D) Asian. (E,F) Caucasian.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

We chose 46 points which are distributed over human face image and use these points for features extraction. The choice of these points is to determine the shape of each element of the face (eyes, eyebrows and mouth), because the shape of these elements is changeable for each emotion, but these changes are different for each race as shown in Figure 2.

Figure 4. Distance between eyebrow and eye.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

Based on what we stated above, we need to extract 28 features, which describe the distances between certain points explained in the previous stage, these features are classified into six groups, and each group describes the features of one face element. All features are a vertical distances between two points. Group one contains seven features for mouth, groups two and three contains 14 features for eyes, groups four and five contain six features for eyebrows, and the last group has one feature only which is the distance between the beginning of the eyebrow and the beginning of the eye in same side, this is significant (from point 23 to 15) because it is used to measure the distance of eyebrow from the eye. This feature is shown in Figure 4 by a line.

Figure 5. ANN Structure 4.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

For classification purpose of the emotions, we use ANN of supervised learning based on backpropagation algorithm. Backpropagation neural network architecture is used with its standards learning function with 28 inputs representing the extracted features and 6 outputs representing 6 emotions, happy, sad, angry, fear, shame and disgust. the emotions. We have also a hidden layer with 16 nodes selected after various trails to obtain the best results. The used ANN is depicted in Figure 5.

Figure 4. Extracted bands diagramClassification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Based on that coefficient, different bands have to be extracted. The bands are alpha, beta, theta, gamma, and delta. Figure 4 shown in below which is represent the different extract band diagrams.

opencc-by-4.0Aug 2015View details →

Figure 3. Electrode placement diagram-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Reference electrode placed AFz placed in between AF1 and AF2 electrode and ground electrode Oz is placed between O1 and O2 electrodes. The impedance of the electrode range is 5KΩ. The sampling rate was fixed range between 256 samples per second for all the channels. Figure 3 shown in below which is represent the electrode placement diagram. The recorded EEG signal is used to recognize the different level of emotions.

opencc-by-4.0Aug 2015View details →

Figure 2. Emotion recognition using EEG-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

This section describes that collection of EEG signals for different emotion recognition experiments. The electroencephalography signals of 32 participants were recorded during one minute videos. Based on that participants are rated in terms of valence and arousal, like/dislike, familiarities and dominance. Emotion related ratings are given based on the online self assessment which is 120 one minute extracted music videos, which are rated by 14-16 volunteers based on arousal and valence. The EEG signals were recorded using 64 electrodes, first 62 electrodes are active electrode, one for reference and remaining one is ground electrode. All the electrodes are placed on the scalp which is made up of the Ag/Ag-Cl. Figure 2 shown in below which is represent the basic EEG signal recording methods and emotion analysis process.

opencc-by-4.0Aug 2015View details →

Figure 1. Brain Structure-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

EEG data have collected from desirable subjects. Each and every EEG signal has different kind of bands like Alpha, Beta, Gamma, Theta, and Delta. Each band stores the particular information about the emotions. Alpha band (8-13 Hz) which located in Frontal Occipital, Beta band (13-30 Hz) which located in Frontal Central, Gamma band (30-100 Hz), Theta band (4- 7 Hz) which located in Midline Temp, Delta band (0-4Hz) which located in Frontal Lobe. Before processing the EEG signal and extracting these bands, preprocess the signal and reduce the noise. The basic brain figure is shown in below.

Figure 8. Sample Emotion Classification Result-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

For all the 32 participants the EEG signal has to be sampled and process their emotions. The emotions are depends on the music and video clips. In this paper the video clips are changed from one person to other person. Here hybrid feed forward neural networks with radial basis function; probabilistic neural network classifier is used to classify the emotions from EEG. PNN is very fast and insensitive neural network which provides the optimized classification result. Compare to the multi layer perception neural network it provide accurate result. It classifies the emotion into two different groups like arousal and valence. Figure 8 shows that the model implemented result of emotion classification and person identification.

Figure 7. Neural Network model-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

In Probabilistic Neural Network the operations are organized into a multilayer feed forward neural network with four layers like input layer, hidden layer, pattern layer and output layer. PNN use the Euclidean distance measure the difference between one neuron to other neurons. The actual target values are stored in the hidden neuron and the optimized weighted values are fed into the same category hidden neuron. Then finally the output layer compared the weighted votes of each target values and the target votes are used to predict the emotions.

Figure 10. Sensitivity and specificity of different bands-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Figure10 has shown in sensitivity and specificity of different neural network which is used to explain how exactly the emotions are classified into groups and accuracy value shown in above table 3.

Figure 9. Mean Square Error for different bands-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Figure 9 has shown in different epochs using neural networks with mean square error performance and the Table 3 shows that different bands mean square error values while training the neural networks with particle swarm optimization.

Figure 6. Mean square error of alpha band-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

Particle swarm optimization algorithm first optimizes the neural networks weight and bias and provides the minimum mean square error with nearer by zero. The following figure 6 has shown that minimum mean square error when training the particular band features.

Figure 5. Process flow of PSO-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

PSO is used to identify the best solution from collection of solution. It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle's position and velocity. Each particle's movement is influenced by its local best known position but, is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. PSO is a metaheuristic as it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. However, metaheuristic such as PSO do not guarantee an optimal solution is ever found. The following Figure 5 explains the basic flow of PSO process.

opencc-by-4.0Jul 2017View details →

BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 5. Emotional flow after 3 hours of graphical editing

To resume, we started with a product interface, found a way to determine two opposite states, than used that way to map user interaction with the product and determine the emotional answer to that interface. The results can then be used to improve product design, to elicit certain emotional responses, etc. For example, in this case, due to the rapid movement (anger) patterns in the aligning phase, a layout that minimizes this can be developed, using a shortcut menu or dynamically appearing guidelines. Furthermore, the user interface can be imagined to be able to learn working patterns and shift the shortcut menu from an aligning menu, if it detects anger patterns, into a color/shape picking menu, if it detects relaxation patterns, therefore assessing the emotional impact and improving the design of the interface in the same time.

opencc-by-nc-sa-4.0Apr 2018View details →

The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)

Description The Ryerson Audio-Visual Database of Emotional Speech and Song (<a href="https://affectivedatascience.com/datasets.html#ravdess">RAVDESS</a>) contains 7356 files (total size: 24.8 GB). The dataset contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression. All conditions are available in three modality formats: Audio-only (16bit, 48kHz .wav), Audio-Video (720p H.264, AAC 48kHz, .mp4), and Video-only (no sound).  Note, there are no song files for Actor_18. The RAVDESS was developed by Dr <a href="https://affectivedatascience.com/people/livingstone_sr" target="_blank" rel="noopener">Steven R. Livingstone</a>, who now leads the <a href="https://affectivedatascience.com">Affective Data Science Lab</a>, and Dr <a href="https://www.torontomu.ca/psychology/about-us/our-people/faculty/frank-russo/" target="_blank" rel="noopener">Frank A. Russo</a> who leads the <a href="https://psychlabs.torontomu.ca/smartlab/">SMART Lab</a>. Citing the RAVDESS The RAVDESS is released under a Creative Commons Attribution license, so please cite the RAVDESS if it is used in your work in any form.  Published academic papers should use the academic paper citation for our PLoS1 paper.  Personal works, such as machine learning projects/blog posts, should provide a URL to this Zenodo page, though a reference to our PLoS1 paper would also be appreciated. Academic paper citation <blockquote> Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>. </blockquote> Personal use citation <blockquote> Include a link to this Zenodo page - <a href="../record/1188976">https://zenodo.org/record/1188976</a> </blockquote> Commercial Licenses Commercial licenses for the RAVDESS can be purchased.  For more information, please visit our <a href="https://psychlabs.torontomu.ca/smartlab/ravdess-commercial-licensing/">license page of fees</a>, or contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20Commercial%20License">ravdess@gmail.com</a>. Contact Information If you would like further information about the RAVDESS, to purchase a commercial license, or if you experience any issues downloading files, please contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20feedback%20from%20Zenodo">ravdess@gmail.com</a>. Example Videos Watch a sample of the RAVDESS <a href="https://www.youtube.com/watch?v=Y7OQoNEu3dY">speech</a> and <a href="https://www.youtube.com/watch?v=XQkmH4oYZkg">song</a> videos. Emotion Classification Users If you're interested in using machine learning to classify emotional expressions with the RAVDESS, please see our new RAVDESS Facial Landmark Tracking data set [<a href="../record/3255102">Zenodo project page</a>]. Construction and Validation Full details on the construction and perceptual validation of the RAVDESS are described in our PLoS ONE paper - <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>. The RAVDESS contains 7356 files. Each file was rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained adult research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity, interrater reliability, and test-retest intrarater reliability were reported. Validation data is open-access, and can be downloaded along with our paper from <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0196391">PLoS ONE</a>. Contents Audio-only files Audio-only files of all actors (01-24) are available as two separate zip files (~200 MB each): <ul> <li>Speech file (Audio_Speech_Actors_01-24.zip, 215 MB) contains 1440 files: 60 trials per actor x 24 actors = 1440. </li> <li>Song file (Audio_Song_Actors_01-24.zip, 198 MB) contains 1012 files: 44 trials per actor x 23 actors = 1012.</li> </ul> Audio-Visual and Video-only files Video files are provided as separate zip downloads for each actor (01-24, ~500 MB each), and are split into separate speech and song downloads: <ul> <li>Speech files (Video_Speech_Actor_01.zip to Video_Speech_Actor_24.zip) collectively contains 2880 files: 60 trials per actor x 2 modalities (AV, VO) x 24 actors = 2880.</li> <li>Song files (Video_Song_Actor_01.zip to Video_Song_Actor_24.zip) collectively contains 2024 files: 44 trials per actor x 2 modalities (AV, VO) x 23 actors = 2024.</li> </ul> File Summary In total, the RAVDESS collection includes 7356 files (2880+2024+1440+1012 files). File naming convention Each of the 7356 RAVDESS files has a unique filename. The filename consists of a 7-part numerical identifier (e.g., 02-01-06-01-02-01-12.mp4). These identifiers define the stimulus characteristics:  Filename identifiers  <ul> <li>Modality (01 = full-AV, 02 = video-only, 03 = audio-only).</li> <li>Vocal channel (01 = speech, 02 = song).</li> <li>Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).</li> <li>Emotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.</li> <li>Statement (01 = "Kids are talking by the door", 02 = "Dogs are sitting by the door").</li> <li>Repetition (01 = 1st repetition, 02 = 2nd repetition).</li> <li>Actor (01 to 24. Odd numbered actors are male, even numbered actors are female).</li> </ul> Filename example: 02-01-06-01-02-01-12.mp4  <ol> <li>Video-only (02)</li> <li>Speech (01)</li> <li>Fearful (06)</li> <li>Normal intensity (01)</li> <li>Statement "dogs" (02)</li> <li>1st Repetition (01)</li> <li>12th Actor (12)</li> <li>Female, as the actor ID number is even.</li> </ol> License information The RAVDESS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>  Commercial licenses for the RAVDESS can also be purchased.  For more information, please visit our <a href="https://smartlaboratory.org/ravdess/commercial-licensing/">license fee page</a>, or contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20Commercial%20License">ravdess@gmail.com.</a> Related Data sets <ul> <li>RAVDESS Facial Landmark Tracking data set [<a href="../record/3255102">Zenodo project page</a>].</li> </ul>

opencc-by-4.0Jun 2018View details →

Slow motion in films and video clips: Music influences perceived duration and emotion, autonomic physiological activation and pupillary responses [data set]

Data set for a study to be published by PLOS ONE.